Tensorflow,谷歌开发者 Josh Gordon自己做了一套视频教程，每月一更新，下面是对应的网站和代码笔记。

Follows the Machine Learning Recipes with Josh Gordon series by Google Developers.

machine-learning-recipes with Josh Gordon

Github-Josh Gordon

Bilibili-video-machine-learning-recipes with Josh Gordon

中文字幕video-machine-learning-recipes with Josh Gordon

tutorials-Josh Gordon

fyuanfen/Machine-Learning-Recipe

Six lines of Python is all it takes to write your first machine learning program! In this episode, we'll briefly introduce what machine learning is and why it's important. Then, we'll follow a recipe for supervised learning (a technique to create a classifier from examples) and code it up.

Last episode, we treated our Decision Tree as a blackbox. In this episode, we'll build one on a real dataset, add code to visualize it, and practice reading it - so you can see how it works under the hood.

More information Decision Trees can be found in http://scikit-learn.org/stable/modules/tree.html

Good features are informative, independent, and simple. In this episode, we'll introduce these concepts by using a histogram to visualize a feature from a toy dataset.

In this episode, we’ll write a basic pipeline for supervised learning with just 12 lines of code. Along the way, we'll talk about training and testing data. Then, we’ll work on our intuition for what it means to “learn” from data.

Welcome back! It's time to write our first classifier. This is a milestone if you’re new to machine learning. We'll start with our code from episode #4 and comment out the classifier we imported. Then, we'll code up a simple replacement - using a scrappy version of k-Nearest Neighbors